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Objectives
- Design and develop a cost-effective UGV for educational use.
- Provide an interactive learning tool for students to understand robotics, automation, and navigation systems.
- Enable students to program and control UGV movements in real-time.
- Integrate sensors and artificial intelligence for enhanced learning.
Design Considerations
- Structure: Compact and modular, supporting sensor and AI integration.
- Locomotion System: Differential drive using DC or stepper motors for smooth movement.
- Control System: Microcontroller-based (Arduino, Raspberry Pi) with beginner-friendly interfaces.
- Navigation & Sensing: Equipped with LiDAR, ultrasonic, or infrared sensors for obstacle detection and avoidance.
- Power Supply: Rechargeable batteries with efficient power management circuitry.
- Communication & Connectivity: Supports Bluetooth or Wi-Fi for remote access and programming.
Key Components
- Microcontroller (Arduino / Raspberry Pi)
- Servo or Stepper Motors
- Motor Drivers
- Rechargeable Power Supply Unit
- Sensors (limit switches, force sensors, etc.)
- 3D-printed or metal frame
- Communication Modules (Bluetooth / Wi-Fi)
Software and Programming
- Languages: Python, C++, Blockly (beginner-friendly)
- Simulation Tools: ROS (Robot Operating System), Gazebo
- Control Methods: Manual, pre-programmed, or AI-based automation
Applications in Education
- Teaching robotics and automation in schools and universities.
- Experimenting with inverse kinematics and motion planning.
- Demonstrating AI and machine learning integration in robotics.
- Facilitating STEM workshops and hands-on training programs.
Challenges & Future Scope
- Reducing cost without compromising performance and precision.
- Improving real-time responsiveness through optimized algorithms.
- Supporting more programming languages and AI models.
- Exploring human-robot collaboration and advanced AI applications.
Conclusion
An Unmanned Guided Vehicle (UGV) for educational purposes serves as a fundamental tool for hands-on learning in robotics and automation. By developing an accessible and functional model, students can gain practical experience in programming, engineering, and problem-solving. Future enhancements in AI integration and adaptability can further expand its applications in education and real-world automation.
Initial Model
